Periparturient production-related metabolic diseases (PRMDs) occur in dairy cows near the time of parturition, which is the time when a cow gives birth. Some examples of factors that are related to PRMDs are: fatty liver, ketosis, mastitis, and displaced abomasums, with or without concurrent illness associated with an infection. Cows are most susceptible to these PRMDs during the rapid increase in milk secretion at the time of parturition, which causes a decrease in blood glucose and insulin concentrations. However, increased energy demand of later fetal growth and lactogenesis cause compensatory prepartum serum non-sterified fatty acids (NEFA) release. PRMDs can cause decreased milk yield, medication costs, and increased culling, or removal, of animals. In the past, efforts have been devoted to identify when most PRMDs occur and to institute management practices to help decrease the incidence of PRMDs in dairy cows. Although progress has been made in understanding the biology of energy metabolism and immune function in transition cows, formulating transition diets, dietary cation-anion difference (CAD), avoiding over-conditioning, and identifying when most PRMDs occur, the incidence of PRMDs has remained stable. Some efforts have included measuring levels of various chemicals in a cow's blood. Correlations have been found linking risk of PRMDs with serum concentrations of free fatty acids (FFAs), NEFAs, triglycerides (TG), beta-hydroxybutyrate (BHBA), as well as hepatic TG to glycogen ratios. Serum concentration of NEFA taken one week before parturition and milk concentration of BHBA taken one week after parturition have been linked to risk of displaced abomasum (LeBlanc, Leslie, & Duffield, 2005). Risk of ketosis and endometritis has been linked to levels of ketones in milk and blood above a certain threshold at one week after parturition (Reist, Erdin, Euw, Tschümperlin, Leuenberger, Hammon, et al., 2003). One study found that levels of vitamin C in plasma were not related to risk of ketosis (Padilla, Shibano, Inoue, Matsui, & Yano, 2005). Previous methods to predict risk of developing PRMDs using many of the relationships found involves an average cost for this battery of tests at approximately $30-50/sample with a requisite submission of 11 or more samples, necessitating an increased expense of obtaining blood from groups of cows to predict herd risk. Also, many of the relationships require waiting for testing until after parturition, when the onset of disease is approaching.
A need, therefore, exists for a method to accurately predict individual cow risk of PRMDs using a minimally or noninvasive procedure that can be performed at or before parturition. It would be desirable to predict risk of PRMDs early to allow for early intervention that would reduce the loss in milk production, cost of medication, and culling of animals.
It has been shown that the ratios of certain isotopes in an organism's tissues are related to the organism's metabolism. Recent studies of stable isotope ratios (2H/1H, 13C/12C, 15N/14N, 18O/16O, and 34S/32S) have yielded information about “dietary habits and the metabolic status of prehistoric and modern humans, eating disorders, forensic medicine, geographical location, and wildlife dietary ecology” (see Petzke, Fuller, & Metges, 2010). Every food source in an animal's diet contains certain ratios of isotopes, such as the ratio of carbon-13 (13C) to carbon-12 (12C). Often these ratios will be different in the animal's tissues due to a process called isotopic fractionation. Isotopic fractionation refers to the alteration of isotope levels as a result of chemical or physical processes. For example, some metabolic processes discriminate against the heavier 13C isotope, and therefore molecules containing the 13C isotope are not metabolized as readily as molecules containing only the 12C isotope. When the animal's body metabolizes energy sources such as lipids and proteins, the level of 13C in the animal's tissue becomes enriched because the body tends to metabolize more lipids and proteins containing 12C, and less containing 13C. In addition to affecting isotope levels in tissue, metabolic processes can affect isotope levels in metabolic byproducts, such as feces, and metabolically-inert substrates, such as hair. Studies examining stable isotopes at or near natural abundance levels are usually reported as delta (6), a value given in parts per thousand or per mil (“o/oo”). Delta values are not absolute isotope abundances but differences between sample readings and one or another of the widely used natural abundance standards which are considered delta=zero (e.g. air for N, At %15N=0.3663033; Pee Dee Belemnite for C, At %13C=1.1112328). Absolute isotope ratios (R) are measured for sample and standard, and the relative measure delta is calculated:δ15N o/oo vs. [std]=((Rsample−Rstd)/Rstd)(1000δo/oo)whereR=(At %15N)/At %14N)For instance, if a leaf sample is found to have a 15N/14N ratio R greater than the standard's by 5 parts per thousand, this value is reported as δ15N=+5 delta o/oo. The transformation of absolute At % values into relative (to a certain standard) delta values is used because the absolute differences between samples and standard are quite small at natural abundance levels and might appear only in the third or fourth decimal place if At % were reported.” (See http://www.uga.edu/sisbl/stable.html, accessed on 30 Mar. 2011).
Ratios of isotopes, such as the ratio of 13C to 12C, are sometimes referred to as isotopic “signatures.” These signatures can differ between individual animals even when the animals consume identical diets. The differences are related to nutrient assimilation, which is regulated by innate metabolism. Differences in innate metabolism may be influenced by an individual animal's genetics. Some animal substrates are metabolically active, meaning that they are constantly being reformed through metabolic processes. These substrates include by-products of metabolism such as breath, urine, and feces. The isotopic signatures in these substrates are constantly changing as they are continuously produced by metabolic processes. Other metabolically active substrates are replaced at particular time intervals, such as erythrocytes, which are replaced approximately every three months, or plasma proteins, which are replaced approximately every two weeks. The isotopic signatures of these substrates are affected by metabolic processes occurring throughout the time interval since they were formed. Metabolically inert substrates include hair, fur, hooves, claws, feathers, and other tissues that do not change after they are formed. These substrates retain the isotopic signatures present at their formation, and so they can provide a historical record relating to the animal's metabolism. For example, every point along a strand of hair may have slightly different isotopic signatures because of changes that occurred in the animal's metabolism while the hair was growing. After the proteins making up the strand of hair are formed, they are never replaced or otherwise affected by metabolic processes, so they retain the same isotopic signature as they had at formation.
Isotopic signatures have been used to learn about the diet of humans and animals. Isotopes in hair of ancient human remains have allowed reconstruction of dietary conditions in ancient human populations (Petzke, Fuller, & Metges, 2010). It has been found that isotope signatures in hair of modern humans are related to excessive intake of meat products (Petzke, Boeing, Klaus, & Metges, 2005). This relationship has potential to be used to correct errors in studies requiring self-reporting of meat intake, because it provides an objective standard against which to check self-reporting.
It would be desirable to provide a method of using isotopic signatures to predict PRMDs in dairy cows which would be accurate, noninvasive, and performable at or before parturition to allow time for early intervention.
Horses treated for gastrointestinal colic sometimes die or continue to have health problems after treatment. It would be desirable to provide a method for predicting the likelihood of continued health problems in order to decide what treatment options would best enhance recovery or to relieve unavoidable pain and suffering with early humane euthanasia.